#### What Will I Learn?

- You will learn Python
- You will learn Decision Tree
- You will learn IF-THEN logic

#### Requirements

- numpy
- scipy
- scikit-learn

#### Difficulty

- Intermediate


#### Tutorial Contents
Hai Everyone, I spent part of an early semester holiday in 2018 to learn Python programming language interpreters. Initially interested in making a simple machine learning that can be trained, many search results recommend to use Python programming language.

After learning the basics and how Python works finally to practice the first case I take is the gender detection of men or women of each character with the help of tutorial here and there. This detector uses the Decision Tree method .

### Decision Tree

For those who are not familiar with the Decision Tree method , simply this method is a classification method to get the conclusion of a data. The process works similar to IF-THEN logic . As an illustration of how the Decision Tree works , an illustration of Dr. The following Saed Sayad can help:

![Decision_rules.png](https://res.cloudinary.com/hpiynhbhq/image/upload/v1521901687/v9xkr0ritblcnoqelf9n.png)

Decision Tree logic can be trained by teaching certain patterns from a number of datasets to make new decisions, new inputs then analyzed to look for specific patterns according to previous training to generate a knowledge that can determine conclusions for new input data 

## Male or Female Detection Cases
In this case, the training data we use is high character data, weight and size of shoes from men and women. With a number of training datasets , Decision Tree will create an analysis of the character patterns of each gender so that when a new character data input application can determine the input data is the character of a man or woman.

## How to make
In Python, there is a package that has provided the decision tree method to use. This package is stored in a bit-learn package . To be able to use scious-learn there are 2 other dependency package that is needed, that is numpy and scipy . These packages can be installed by using the #pip install [package] command via terminal / cmd. In general the installation package as follows:

- $ pip install numpy
- $ pip install scipy
- $ pip install -U scikit-learn

## Coding
Simply coding for this application can be done on one file only, create a file with the name demo.py, with the contents:

``` 
from sklearn import tree # import scikit-learn untuk decision tree

# sample data format [height, weight, size-shoe]
# data size of 11 people
data = [[181,80,44], [177,70,43], [160,60,38], [154,54,37], 
  [166,65,40], [190,90,47], [175,64,39], [165,49,40],
  [171,75,42], [157,55,39], [181,85,43],]

# data gender,berurut sesuai dengan datanya (merujuk variabel data)
gender = ['male', 'male', 'female', 'female', 
   'female', 'male', 'male', 'female', 
   'male', 'female', 'male',]

# calling the DecisionTreeClassifier () method from the tree side
klasifikasi = tree.DecisionTreeClassifier()
# training data, calling methods fit (param), param = data and gender
klasifikasi = klasifikasi.fit(data,gender)

# inserting new data to predict
# invokes the predict method ([data])
databaru = [170,59,41]
prediksi = klasifikasi.predict([databaru])

# print predicted results
print(data)
# print(type(data))
print(prediksi)
```

There are several important parts of the code above:
- First we call the package to be used which is scikit-learn with 
``` 
from sklearn import tree
```
- Contains gender character data on variable
``` 
data
``` 
- contains the gender character pair in the
``` 
gender
``` 
variable
- Conduct training data with:
``` 
# calling the DecisionTreeClassifier () method from the tree sideklasifikasi = tree.DecisionTreeClassifier()
# training data, calling methods fit (param), param = data and gender
klasifikasi = klasifikasi.fit(data,gender)
```
- Catch new data for prediction:
``` 
# inserting new data to predict
# invokes the predict method ([data])
databaru = [170,59,41]
prediksi = klasifikasi.predict([databaru])
```
- then print the result with
``` 
print
```
command

## Results
![image.png](https://res.cloudinary.com/hpiynhbhq/image/upload/v1521902293/wkfoyl7asbmvoss5fuoi.png)

The above is one way to create a simple male-female detector with each character input.
So with the above tools we are able to predict a person with character traits such as height, weight, size-shoes. Okay, thanks to everyone who has seen the tutorial "Creating Gender Detector (Male / Female) in Python"


<br /><hr/><em>Posted on <a href="https://utopian.io/utopian-io/@luky/creating-gender-detector-male-female-in-python">Utopian.io -  Rewarding Open Source Contributors</a></em><hr/>